The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.
Machine learning may be implemented based on a set of training data to train potentially complex models and algorithms for making predictions and further based on a set of test data to measure accuracies and robustness in the predictions made with the complex models and algorithms as trained with the set of training data. The accuracies and robustness in the predictions in machine learning may be largely dependent on whether the set of training data and/or the set of test data is sufficiently large. Thus, for a few large-scale companies or entities such as Google, Facebook, or Uber that own big data, machine learning can be relatively effectively implemented and used for their specific applications.
For a wide variety of other companies, entities and/or individuals and for a wide variety of general or specific applications, however, sufficiently large sets of training and test data may be out of reach, especially at an initial deployment stage of systems when large numbers of feedbacks have yet to be collected by the systems implementing artificial intelligence (AI) or machine learning (ML). As a result, it may take a long time, a lot of resources, and a large amount of investment before such a system becomes accurate and robust under currently available approaches.